Purpose:

Runs survival analysis models using splicing cluster assignment and 1) single exon splicing burden index (SBI) 2) KEGG Spliceosome GSVA scores or 3) CLK1 exon 4 TPM as a predictor

Usage

Uses a wrapper function (survival_analysis) from utils folder.

Setup

Packages and functions

Load packages, set directory paths and call setup script

library(tidyverse)
library(survival)
library(ggpubr)
library(ggplot2)
library(patchwork)

root_dir <- rprojroot::find_root(rprojroot::has_dir(".git"))

data_dir <- file.path(root_dir, "data")
analysis_dir <- file.path(root_dir, "analyses", "survival")
input_dir <- file.path(analysis_dir, "results")
results_dir <- file.path(analysis_dir, "results")
plot_dir <- file.path(analysis_dir, "plots")

# If the input and results directories do not exist, create it
if (!dir.exists(results_dir)) {
  dir.create(results_dir, recursive = TRUE)
}

source(file.path(analysis_dir, "util", "survival_models.R"))

Set metadata and cluster assignment file paths

metadata_file <- file.path(input_dir, "splicing_indices_with_survival.tsv")

cluster_file <- file.path(root_dir, "analyses",
                          "sample-psi-clustering", "results",
                          "sample-cluster-metadata-top-5000-events-stranded.tsv")

kegg_scores_stranded_file <- file.path(root_dir, "analyses",
                          "sample-psi-clustering", "results",
                          "gsva_output_stranded.tsv")

tpm_file <- file.path(data_dir, "rna-isoform-expression-rsem-tpm.rds")
clk1_psi_file <- file.path(root_dir, 
                           "analyses", 
                           "CLK1-splicing_correlations", 
                           "results", 
                           "clk1-exon4-psi.tsv")

Wrangle data Add cluster assignment and spliceosome gsva scores to metadata and define column lgg_group (LGG or non_LGG)

metadata <- read_tsv(metadata_file)
Rows: 684 Columns: 26
── Column specification ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Delimiter: "\t"
chr (11): Kids_First_Biospecimen_ID, Histology, Kids_First_Participant_ID, molecular_subtype, extent_of_tumor_resection, EFS_event_type, OS_statu...
dbl (15): Total, AS_neg, AS_pos, AS_total, SI_A3SS, SI_A5SS, SI_RI, SI_SE, SI_Total, EFS_days, OS_days, age_at_diagnosis_days, age_at_diagnosis, ...

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
clusters <- read_tsv(cluster_file) %>%
  dplyr::rename(Kids_First_Biospecimen_ID = sample_id)
Rows: 729 Columns: 8
── Column specification ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Delimiter: "\t"
chr (6): sample_id, plot_group, plot_group_hex, RNA_library, molecular_subtype, plot_group_n
dbl (2): cluster, group_n

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
clk1_psi <- read_tsv(clk1_psi_file) %>%
  dplyr::rename(CLK1_ex4_PSI = PSI) %>%
  select(-plot_group)
Rows: 729 Columns: 3
── Column specification ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Delimiter: "\t"
chr (2): Kids_First_Biospecimen_ID, plot_group
dbl (1): PSI

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
gsva_scores <- read_tsv(kegg_scores_stranded_file) %>%
  dplyr::filter(geneset == "KEGG_SPLICEOSOME") %>%
  dplyr::rename(spliceosome_gsva_score = score)
Rows: 22599 Columns: 3
── Column specification ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Delimiter: "\t"
chr (2): sample_id, geneset
dbl (1): score

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
all_clk4_transcr_counts <- readRDS(tpm_file) %>%
  filter(grepl("^CLK1", gene_symbol)) %>%
  mutate(
    transcript_id = case_when(
      transcript_id %in% c("ENST00000321356.9", "ENST00000434813.3", "ENST00000409403.6") ~ "Exon 4",
#      transcript_id == "ENST00000321356.9" ~ "Exon 4",
      TRUE ~ "Other"
    )
  ) %>%
  group_by(transcript_id) %>%
  summarise(across(starts_with("BS"), sum, na.rm = TRUE)) %>%
  pivot_longer(cols = -transcript_id, names_to = "Kids_First_Biospecimen_ID", values_to = "CLK1_Ex4_TPM") %>%
  filter(transcript_id == "Exon 4") %>%
  inner_join(clusters, by = "Kids_First_Biospecimen_ID") %>%
  left_join(clk1_psi)
Joining with `by = join_by(Kids_First_Biospecimen_ID)`
# how many clusters?
n_clust <- length(unique(clusters$cluster))

metadata <- metadata %>%
  right_join(all_clk4_transcr_counts %>% dplyr::select(Kids_First_Biospecimen_ID,
                                       cluster, CLK1_Ex4_TPM, CLK1_ex4_PSI)) %>%
  left_join(gsva_scores %>% dplyr::select(sample_id,
                                          spliceosome_gsva_score),
            by = c("Kids_First_Biospecimen_ID" = "sample_id")) %>% 
  dplyr::mutate(cluster = glue::glue("Cluster {cluster}")) %>%
  dplyr::mutate(cluster = fct_relevel(cluster,
                                               paste0("Cluster ", 1:n_clust))) %>%
  dplyr::mutate(lgg_group = case_when(
    plot_group == "Low-grade glioma" ~ "LGG",
    TRUE ~ "non-LGG"
  )) %>%
  dplyr::mutate(SBI = SI_Total * 10) %>%
  dplyr::mutate(age_at_diagnosis_years = age_at_diagnosis_days/365.25)
Joining with `by = join_by(Kids_First_Biospecimen_ID)`

Generate coxph models including extent of tumor resection, lgg group, cluster assignment, SBI, and CLK1 exon 4 TPM as covariates

add_model_os <- fit_save_model(metadata[!metadata$extent_of_tumor_resection %in% c("Not Reported", "Unavailable"),],
                              terms = "extent_of_tumor_resection+lgg_group+cluster+age_at_diagnosis_years+SBI+CLK1_Ex4_TPM",
                               file.path(results_dir, "cox_OS_additive_terms_resection_lgg_group_cluster_SBI_CLK1_Ex4_TPM.RDS"),
                               "multivariate",
                               years_col = "OS_years",
                               status_col = "OS_status")

forest_os <- plotForest(readRDS(file.path(results_dir, "cox_OS_additive_terms_resection_lgg_group_cluster_SBI_CLK1_Ex4_TPM.RDS")))
`height` was translated to `width`.
Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).

forest_os

ggsave(file.path(plot_dir, "forest_add_OS_resection_lgg_group_cluster_assignment_SBI_CLK1_Ex4_TPM.pdf"),
       forest_os,
       width = 10, height = 6, units = "in",
       device = "pdf")


add_model_efs <- fit_save_model(metadata[!metadata$extent_of_tumor_resection %in% c("Not Reported", "Unavailable"),],
                              terms = "extent_of_tumor_resection+lgg_group+cluster+age_at_diagnosis_years+SBI+CLK1_Ex4_TPM",
                               file.path(results_dir, "cox_EFS_additive_terms_resection_lgg_group_cluster_SBI_CLK1_Ex4_TPM.RDS"),
                               "multivariate",
                               years_col = "EFS_years",
                               status_col = "EFS_status")

forest_efs <- plotForest(readRDS(file.path(results_dir, "cox_EFS_additive_terms_resection_lgg_group_cluster_SBI_CLK1_Ex4_TPM.RDS")))
`height` was translated to `width`.
Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).

forest_efs

ggsave(file.path(plot_dir, "forest_add_EFS_resection_lgg_group_cluster_assignment_SBI_CLK1_Ex4_TPM.pdf"),
       forest_efs,
       width = 10, height = 6, units = "in",
       device = "pdf")

repeat analysis with CLK1 exon 4 TPM alone

add_model_os <- fit_save_model(metadata[!metadata$extent_of_tumor_resection %in% c("Not Reported", "Unavailable"),],
                              terms = "extent_of_tumor_resection+lgg_group+cluster+age_at_diagnosis_years+CLK1_Ex4_TPM",
                               file.path(results_dir, "cox_OS_additive_terms_resection_lgg_group_cluster_CLK1_Ex4_TPM.RDS"),
                               "multivariate",
                               years_col = "OS_years",
                               status_col = "OS_status")

forest_os <- plotForest(readRDS(file.path(results_dir, "cox_OS_additive_terms_resection_lgg_group_cluster_CLK1_Ex4_TPM.RDS")))
`height` was translated to `width`.
Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).

forest_os

ggsave(file.path(plot_dir, "forest_add_OS_resection_lgg_group_cluster_assignment_CLK1_Ex4_TPM.pdf"),
       forest_os,
       width = 10, height = 6, units = "in",
       device = "pdf")



add_model_efs <- fit_save_model(metadata[!metadata$extent_of_tumor_resection %in% c("Not Reported", "Unavailable"),],
                              terms = "extent_of_tumor_resection+lgg_group+cluster+age_at_diagnosis_years+CLK1_Ex4_TPM",
                               file.path(results_dir, "cox_EFS_additive_terms_resection_lgg_group_cluster_CLK1_Ex4_TPM.RDS"),
                               "multivariate",
                               years_col = "EFS_years",
                               status_col = "EFS_status")

forest_efs <- plotForest(readRDS(file.path(results_dir, "cox_EFS_additive_terms_resection_lgg_group_cluster_CLK1_Ex4_TPM.RDS")))
`height` was translated to `width`.
Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).

forest_efs

ggsave(file.path(plot_dir, "forest_add_EFS_resection_lgg_group_cluster_assignment_CLK1_Ex4_TPM.pdf"),
       forest_efs,
       width = 10, height = 6, units = "in",
       device = "pdf")

NA
NA

repeat analysis with CLK1 exon 4 PSI

add_model_os <- fit_save_model(metadata[!metadata$extent_of_tumor_resection %in% c("Not Reported", "Unavailable"),],
                              terms = "extent_of_tumor_resection+lgg_group+cluster+age_at_diagnosis_years+CLK1_ex4_PSI",
                               file.path(results_dir, "cox_OS_additive_terms_resection_lgg_group_cluster_CLK1_ex4_PSI.RDS"),
                               "multivariate",
                               years_col = "OS_years",
                               status_col = "OS_status")

forest_os <- plotForest(readRDS(file.path(results_dir, "cox_OS_additive_terms_resection_lgg_group_cluster_CLK1_ex4_PSI.RDS")))
`height` was translated to `width`.
Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).

forest_os

ggsave(file.path(plot_dir, "forest_add_OS_resection_lgg_group_cluster_assignment_CLK1_ex4_PSI.pdf"),
       forest_os,
       width = 10, height = 6, units = "in",
       device = "pdf")


add_model_efs <- fit_save_model(metadata[!metadata$extent_of_tumor_resection %in% c("Not Reported", "Unavailable"),],
                              terms = "extent_of_tumor_resection+lgg_group+cluster+age_at_diagnosis_years+CLK1_ex4_PSI",
                               file.path(results_dir, "cox_EFS_additive_terms_resection_lgg_group_cluster_CLK1_ex4_PSI.RDS"),
                               "multivariate",
                               years_col = "EFS_years",
                               status_col = "EFS_status")

forest_efs <- plotForest(readRDS(file.path(results_dir, "cox_EFS_additive_terms_resection_lgg_group_cluster_CLK1_ex4_PSI.RDS")))
`height` was translated to `width`.
Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).

forest_efs

ggsave(file.path(plot_dir, "forest_add_EFS_resection_lgg_group_cluster_assignment_CLK1_ex4_PSI.pdf"),
       forest_efs,
       width = 10, height = 6, units = "in",
       device = "pdf")

Interaction with GSVA, SBI, CLK1

models <- c("spliceosome_gsva_score", "SBI", "CLK1_Ex4_TPM", "CLK1_ex4_PSI")
# by cluster
for (each in models) {
  int_model_efs <- fit_save_model(metadata[!metadata$extent_of_tumor_resection %in% c("Not Reported", "Unavailable"),],
                                terms = paste0("extent_of_tumor_resection+lgg_group+cluster*", each, "+age_at_diagnosis_years"),
                                 file.path(results_dir, paste0("cox_EFS_interaction_terms_resection_lgg_group_cluster_", each, ".RDS")),
                                 "multivariate",
                                 years_col = "EFS_years",
                                 status_col = "EFS_status")
  
  int_forest_efs <- plotForest(readRDS(file.path(results_dir, paste0("cox_EFS_interaction_terms_resection_lgg_group_cluster_", each, ".RDS"))))
  
  int_forest_efs
  
  ggsave(file.path(plot_dir, paste0("forest_int_EFS_resection_lgg_group_cluster_assignment_", each, ".pdf")),
         int_forest_efs,
         width = 10, height = 6, units = "in",
         device = "pdf")

  int_model_os <- fit_save_model(metadata[!metadata$extent_of_tumor_resection %in% c("Not Reported", "Unavailable"),],
                                terms = paste0("extent_of_tumor_resection+lgg_group+cluster*", each, "+age_at_diagnosis_years"),
                                 file.path(results_dir, paste0("cox_OS_interaction_terms_resection_lgg_group_cluster_", each, ".RDS")),
                                 "multivariate",
                                 years_col = "OS_years",
                                 status_col = "OS_status")
  
  int_forest_os <- plotForest(readRDS(file.path(results_dir, paste0("cox_OS_interaction_terms_resection_lgg_group_cluster_", each, ".RDS"))))
  
  int_forest_os
  
  ggsave(file.path(plot_dir, paste0("forest_int_OS_resection_lgg_group_cluster_assignment_", each, ".pdf")),
         int_forest_os,
         width = 10, height = 6, units = "in",
         device = "pdf")
}

## clk1 x age
  int_model_efs <- fit_save_model(metadata[!metadata$extent_of_tumor_resection %in% c("Not Reported", "Unavailable"),],
                                terms = paste0("extent_of_tumor_resection+lgg_group+cluster+CLK1_Ex4_TPM*age_at_diagnosis_years"),
                                 file.path(results_dir, paste0("cox_EFS_interaction_terms_resection_lgg_group_cluster_CLK1_Ex4_TPM_age.RDS")),
                                 "multivariate",
                                 years_col = "EFS_years",
                                 status_col = "EFS_status")
  
  int_forest_efs <- plotForest(readRDS(file.path(results_dir, paste0("cox_EFS_interaction_terms_resection_lgg_group_cluster_CLK1_Ex4_TPM_age.RDS"))))
  
  int_forest_efs
  
  ggsave(file.path(plot_dir, paste0("forest_int_EFS_resection_lgg_group_cluster_clk1_age.pdf")),
         int_forest_efs,
         width = 10, height = 6, units = "in",
         device = "pdf")

  int_model_os <- fit_save_model(metadata[!metadata$extent_of_tumor_resection %in% c("Not Reported", "Unavailable"),],
                                terms = paste0("extent_of_tumor_resection+lgg_group+cluster+CLK1_Ex4_TPM*age_at_diagnosis_years"),
                                 file.path(results_dir, paste0("cox_OS_interaction_terms_resection_lgg_group_cluster_clk1_age.RDS")),
                                 "multivariate",
                                 years_col = "OS_years",
                                 status_col = "OS_status")
  
  int_forest_os <- plotForest(readRDS(file.path(results_dir, paste0("cox_OS_interaction_terms_resection_lgg_group_cluster_clk1_age.RDS"))))
  
  int_forest_os
  
  ggsave(file.path(plot_dir, paste0("forest_int_OS_resection_lgg_group_cluster_clk1_age.pdf")),
         int_forest_os,
         width = 10, height = 6, units = "in",
         device = "pdf")

models2 <- c("SBI", "CLK1_Ex4_TPM")
for (each in models2) {
  #### by spliceosome_gsva_score
  int_model_efs <- fit_save_model(metadata[!metadata$extent_of_tumor_resection %in% c("Not Reported", "Unavailable"),],
                                terms = paste0("extent_of_tumor_resection+lgg_group+cluster+spliceosome_gsva_score*", each, "+age_at_diagnosis_years"),
                                 file.path(results_dir, paste0("cox_EFS_interaction_terms_resection_lgg_group_cluster_spliceosome_gsva_score_", each, ".RDS")),
                                 "multivariate",
                                 years_col = "EFS_years",
                                 status_col = "EFS_status")
  
  int_forest_efs <- plotForest(readRDS(file.path(results_dir, paste0("cox_EFS_interaction_terms_resection_lgg_group_cluster_spliceosome_gsva_score_", each, ".RDS"))))
  
  int_forest_efs
  
  ggsave(file.path(plot_dir, paste0("cox_EFS_interaction_terms_resection_lgg_group_cluster_spliceosome_gsva_score_", each, ".pdf")),
         int_forest_efs,
         width = 10, height = 6, units = "in",
         device = "pdf")
}
  


add_model_efs <- fit_save_model(metadata[!metadata$extent_of_tumor_resection %in% c("Not Reported", "Unavailable"),],
                              terms = "extent_of_tumor_resection+lgg_group+cluster+age_at_diagnosis_years+spliceosome_gsva_score+CLK1_Ex4_TPM",
                               file.path(results_dir, "cox_EFS_additive_terms_resection_lgg_group_cluster_spliceosome_score_CLK1_Ex4_TPM.RDS"),
                               "multivariate",
                               years_col = "EFS_years",
                               status_col = "EFS_status")

forest_efs <- plotForest(readRDS(file.path(results_dir, "cox_EFS_additive_terms_resection_lgg_group_cluster_spliceosome_score_CLK1_Ex4_TPM.RDS")))

forest_efs

ggsave(file.path(plot_dir, "forest_add_EFS_resection_lgg_group_cluster_assignment_spliceosome_score_CLK1_Ex4_TPM.pdf"),
       forest_efs,
       width = 10, height = 6, units = "in",
       device = "pdf")


add_model_os <- fit_save_model(metadata[!metadata$extent_of_tumor_resection %in% c("Not Reported", "Unavailable"),],
                              terms = "extent_of_tumor_resection+lgg_group+cluster+age_at_diagnosis_years+spliceosome_gsva_score+CLK1_Ex4_TPM",
                               file.path(results_dir, "cox_OS_additive_terms_resection_lgg_group_cluster_spliceosome_score_CLK1_Ex4_TPM.RDS"),
                               "multivariate",
                               years_col = "EFS_years",
                               status_col = "EFS_status")

forest_os <- plotForest(readRDS(file.path(results_dir, "cox_OS_additive_terms_resection_lgg_group_cluster_spliceosome_score_CLK1_Ex4_TPM.RDS")))

forest_os

ggsave(file.path(plot_dir, "forest_add_OS_resection_lgg_group_cluster_assignment_spliceosome_score_CLK1_Ex4_TPM.pdf"),
       forest_efs,
       width = 10, height = 6, units = "in",
       device = "pdf")

Filter for clusters

cluster_list <- unique(metadata$cluster)

for (each in cluster_list) {
cluster_df <- metadata %>%
  dplyr::filter(cluster == each,
                !is.na(EFS_days)) %>%
  dplyr::mutate(CLK1_TPM_group = case_when(
      CLK1_Ex4_TPM > summary(CLK1_Ex4_TPM)["3rd Qu."] ~ "High CLK1 TPM",
      CLK1_Ex4_TPM < summary(CLK1_Ex4_TPM)["1st Qu."] ~ "Low CLK1 TPM",
      TRUE ~ NA_character_),
      CLK1_PSI_group = case_when(CLK1_ex4_PSI > summary(CLK1_ex4_PSI)["3rd Qu."] ~ "High CLK1 PSI",
      CLK1_ex4_PSI < summary(CLK1_ex4_PSI)["1st Qu."] ~ "Low CLK1 PSI",
      TRUE ~ NA_character_
    )) %>%
  dplyr::mutate(CLK1_TPM_group = fct_relevel(CLK1_TPM_group,
                                                 c("Low CLK1 TPM", "High CLK1 TPM")),
                CLK1_PSI_group = fct_relevel(CLK1_PSI_group,
                                                 c("Low CLK1 PSI", "High CLK1 PSI")))

safe_each <- gsub("[^A-Za-z0-9_-]+", "_", each)

# Generate KM models with `CLK1_TPM_group` as covariate
# Generate kaplan meier survival models for OS and EFS, and save outputs
cluster_clk_tpm_kap_os <- survival_analysis(
  metadata  = cluster_df %>% 
    dplyr::filter(!is.na(CLK1_TPM_group)),
  ind_var = "CLK1_TPM_group",
  test = "kap.meier",
  metadata_sample_col = "Kids_First_Biospecimen_ID",
  days_col = "OS_days",
  status_col = "OS_status"
)

readr::write_rds(cluster_clk_tpm_kap_os,
                 file.path(results_dir, paste0( "logrank_", safe_each, "_OS_clk1_tpm_group.RDS")))

cluster_clk_tpm_kap_efs <- survival_analysis(
  metadata  = cluster_df %>% 
    dplyr::filter(!is.na(CLK1_TPM_group)),
  ind_var = "CLK1_TPM_group",
  test = "kap.meier",
  metadata_sample_col = "Kids_First_Biospecimen_ID",
  days_col = "EFS_days",
  status_col = "EFS_status"
)

readr::write_rds(cluster_clk_tpm_kap_efs,
                 file.path(results_dir, paste0( "logrank_", safe_each, "_EFS_clk1_tpm_group.RDS")))

# Generate KM models with `CLK1_PSI_group` as covariate
# Generate kaplan meier survival models for OS and EFS, and save outputs
cluster_clk_psi_kap_os <- survival_analysis(
  metadata  = cluster_df %>% 
    dplyr::filter(!is.na(CLK1_PSI_group)),
  ind_var = "CLK1_PSI_group",
  test = "kap.meier",
  metadata_sample_col = "Kids_First_Biospecimen_ID",
  days_col = "OS_days",
  status_col = "OS_status"
)

readr::write_rds(cluster_clk_psi_kap_os,
                 file.path(results_dir, paste0( "logrank_", safe_each, "_OS_clk1_psi_group.RDS")))

cluster_clk_psi_kap_efs <- survival_analysis(
  metadata  = cluster_df %>% 
    dplyr::filter(!is.na(CLK1_PSI_group)),
  ind_var = "CLK1_PSI_group",
  test = "kap.meier",
  metadata_sample_col = "Kids_First_Biospecimen_ID",
  days_col = "EFS_days",
  status_col = "EFS_status"
)

readr::write_rds(cluster_clk_psi_kap_efs,
                 file.path(results_dir, paste0( "logrank_", safe_each, "_EFS_clk1_psi_group.RDS")))

# Generate cluster KM SI_group plots

km_cluster_clk_tpm_os_plot <- plotKM(model = cluster_clk_tpm_kap_os,
                    variable = "CLK1_TPM_group",
                    combined = F, 
                    title = paste0(each, ", overall survival"),
                    p_pos = "topright")

ggsave(file.path(plot_dir, paste0("km_", each, "_OS_clk1_tpm_group.pdf")),
       km_cluster_clk_tpm_os_plot,
       width = 8, height = 5, units = "in",
       device = "pdf")

km_cluster_clk1_tpm_efs_plot <- plotKM(model = cluster_clk_tpm_kap_efs,
                    variable = "CLK1_TPM_group",
                    combined = F, 
                    title = paste0(each, ", event-free survival"),
                    p_pos = "topright")

ggsave(file.path(plot_dir, paste0( "km_", each, "_EFS_clk1_tpm_group.pdf")), 
       km_cluster_clk1_tpm_efs_plot,
       width = 8, height = 5, units = "in",
       device = "pdf")

km_cluster_clk1_psi_os_plot <- plotKM(model = cluster_clk_psi_kap_os,
                    variable = "CLK1_PSI_group",
                    combined = F, 
                    title = paste0(each, ", overall survival"),
                    p_pos = "topright")

ggsave(file.path(plot_dir, paste0( "km_", each, "_OS_clk1_psi_group.pdf")),
       km_cluster_clk1_psi_os_plot,
       width = 8, height = 5, units = "in",
       device = "pdf")

km_cluster_clk1_psi_efs_plot <- plotKM(model = cluster_clk_psi_kap_efs,
                    variable = "CLK1_PSI_group",
                    combined = F, 
                    title = paste0(each, ", event-free survival"),
                    p_pos = "topright")

ggsave(file.path(plot_dir, paste0("km_", each, "_EFS_clk1_psi_group.pdf")), 
       km_cluster_clk1_psi_efs_plot,
       width = 8, height = 5, units = "in",
       device = "pdf")

# Assess EFS and OS by CLK1 TPM group in multivariate models and generate forest plots

add_model_cluster_efs <- fit_save_model(cluster_df %>% 
                                      dplyr::filter(extent_of_tumor_resection != "Unavailable",
                                                    CLK1_TPM_group %in% c("High CLK1 TPM", "Low CLK1 TPM")
                                                    ),
                                      # %>%
                                     # dplyr::mutate(plot_group = fct_relevel(plot_group,
                                      #                                      c("Other high-grade glioma", "Atypical Teratoid Rhabdoid Tumor",
                                       #                                       "DIPG or DMG", "Ependymoma", "Mesenchymal tumor",
                                        #                                      "Other CNS embryonal tumor", "Low-grade glioma"))),
                              terms = "extent_of_tumor_resection+age_at_diagnosis_years+plot_group+CLK1_TPM_group",
                               file.path(results_dir, paste0( "cox_EFS_additive_terms_subtype_", safe_each, "_clk1_tpm_group.RDS")),
                               "multivariate",
                               years_col = "EFS_years",
                               status_col = "EFS_status")

forest_cluster_clk1_efs <- plotForest(readRDS(file.path(results_dir, paste0("cox_EFS_additive_terms_subtype_", safe_each, "_clk1_tpm_group.RDS"))))

ggsave(file.path(plot_dir, paste0("forest_add_EFS_", safe_each, "_histology_resection_clk1_tpm_group.pdf")),
       forest_cluster_clk1_efs,
       width = 9, height = 4, units = "in",
       device = "pdf")

add_model_cluster_os <- fit_save_model(cluster_df %>% 
                                    dplyr::filter(!extent_of_tumor_resection %in% c("Not Reported", "Unavailable")),
                                    # %>%
                              #      dplyr::mutate(plot_group = fct_relevel(plot_group,
                               #                                             c("Other high-grade glioma", "Atypical Teratoid Rhabdoid Tumor",
                                #                                              "DIPG or DMG", "Ependymoma", "Mesenchymal tumor",
                                 #                                             "Other CNS embryonal tumor", "Low-grade glioma"))),
                              terms = "extent_of_tumor_resection+age_at_diagnosis_years+plot_group+CLK1_TPM_group",
                               file.path(results_dir, paste0( "cox_OS_additive_terms_subtype_", safe_each, "_clk1_tpm_group.RDS")),
                               "multivariate",
                               years_col = "OS_years",
                               status_col = "OS_status")

forest_cluster_clk1_os <- plotForest(readRDS(file.path(results_dir, paste0( "cox_OS_additive_terms_subtype_", safe_each, "_clk1_tpm_group.RDS"))))

ggsave(file.path(plot_dir, paste0( "forest_add_OS_", safe_each, "_histology_resection_clk1_tpm_group.pdf")),
       forest_cluster_clk1_os,
       width = 9, height = 4, units = "in",
       device = "pdf")

#Assess EFS and OS by CLK1 PSI group in multivariate models and generate forest plots

add_model_cluster_efs <- fit_save_model(cluster_df %>% 
                                      dplyr::filter(extent_of_tumor_resection != "Unavailable",
                                                    CLK1_PSI_group %in% c("High CLK1 PSI", "Low CLK1 PSI")),
                                      # %>%
                                     # dplyr::mutate(plot_group = fct_relevel(plot_group,
                                      #                                      c("Other high-grade glioma", "Atypical Teratoid Rhabdoid Tumor",
                                       #                                       "DIPG or DMG", "Ependymoma", "Mesenchymal tumor",
                                        #                                      "Other CNS embryonal tumor", "Low-grade glioma"))),
                              terms = "extent_of_tumor_resection+age_at_diagnosis_years+plot_group+CLK1_PSI_group",
                               file.path(results_dir, paste0( "cox_EFS_additive_terms_subtype_", safe_each, "_clk1_psi_group.RDS")),
                               "multivariate",
                               years_col = "EFS_years",
                               status_col = "EFS_status")

forest_cluster_clk1_efs <- plotForest(readRDS(file.path(results_dir, paste0( "cox_EFS_additive_terms_subtype_", safe_each, "_clk1_psi_group.RDS"))))

ggsave(file.path(plot_dir, paste0( "forest_add_EFS_", safe_each, "_histology_resection_clk1_psi_group.pdf")),
       forest_cluster_clk1_efs,
       width = 9, height = 4, units = "in",
       device = "pdf")

add_model_cluster_os <- fit_save_model(cluster_df %>% 
                                    dplyr::filter(!extent_of_tumor_resection %in% c("Not Reported", "Unavailable")), 
                                    # %>%
                                  #  dplyr::mutate(plot_group = fct_relevel(plot_group,
                                   #                                         c("Other high-grade glioma", "Atypical Teratoid Rhabdoid Tumor",
                                    #                                          "DIPG or DMG", "Ependymoma", "Mesenchymal tumor",
                                     #                                         "Other CNS embryonal tumor", "Low-grade glioma"))),
                              terms = "extent_of_tumor_resection+age_at_diagnosis_years+plot_group+CLK1_PSI_group",
                               file.path(results_dir, paste0( "cox_OS_additive_terms_subtype_", safe_each, "_clk1_psi_group.RDS")),
                               "multivariate",
                               years_col = "OS_years",
                               status_col = "OS_status")

forest_cluster_clk1_os <- plotForest(readRDS(file.path(results_dir, paste0( "cox_OS_additive_terms_subtype_", safe_each, "_clk1_psi_group.RDS"))))

ggsave(file.path(plot_dir, paste0( "forest_add_OS_", safe_each, "_histology_resection_clk1_psi_group.pdf")),
       forest_cluster_clk1_os,
       width = 9, height = 4, units = "in",
       device = "pdf")

# Assess EFS and OS by CLK1 ex 4 TPM in multivariate models and generate forest plots

add_model_cluster_efs <- fit_save_model(cluster_df %>% 
                                      dplyr::filter(extent_of_tumor_resection != "Unavailable"), 
                                   #   %>%
                                    #  dplyr::mutate(plot_group = fct_relevel(plot_group,
                                     #                                       c("Other high-grade glioma", "Atypical Teratoid Rhabdoid Tumor",
                                      #                                        "DIPG or DMG", "Ependymoma", "Mesenchymal tumor",
                                       #                                       "Other CNS embryonal tumor", "Low-grade glioma"))),
                              terms = "extent_of_tumor_resection+age_at_diagnosis_years+plot_group+CLK1_Ex4_TPM",
                               file.path(results_dir, paste0( "cox_EFS_additive_terms_subtype_", safe_each, "_clk1_tpm.RDS")),
                               "multivariate",
                               years_col = "EFS_years",
                               status_col = "EFS_status")

forest_cluster_clk1_efs <- plotForest(readRDS(file.path(results_dir, paste0( "cox_EFS_additive_terms_subtype_", safe_each, "_clk1_tpm.RDS"))))

ggsave(file.path(plot_dir, paste0( "forest_add_EFS_", safe_each, "_histology_resection_clk1_tpm.pdf")),
       forest_cluster_clk1_efs,
       width = 9, height = 4, units = "in",
       device = "pdf")

add_model_cluster_os <- fit_save_model(cluster_df %>% 
                                    dplyr::filter(!extent_of_tumor_resection %in% c("Not Reported", "Unavailable")), 
                                    #%>%
                                 #   dplyr::mutate(plot_group = fct_relevel(plot_group,
                                  #                                          c("Other high-grade glioma", "Atypical Teratoid Rhabdoid Tumor",
                                   #                                           "DIPG or DMG", "Ependymoma", "Mesenchymal tumor",
                                    #                                          "Other CNS embryonal tumor", "Low-grade glioma"))),
                              terms = "extent_of_tumor_resection+age_at_diagnosis_years+plot_group+CLK1_Ex4_TPM",
                               file.path(results_dir, paste0( "cox_OS_additive_terms_subtype_", safe_each, "_clk1_tpm.RDS")),
                               "multivariate",
                               years_col = "OS_years",
                               status_col = "OS_status")

forest_cluster_clk1_os <- plotForest(readRDS(file.path(results_dir, paste0( "cox_OS_additive_terms_subtype_", safe_each, "_clk1_tpm.RDS"))))

ggsave(file.path(plot_dir, paste0( "forest_add_OS_", safe_each, "_histology_resection_clk1_tpm.pdf")),
       forest_cluster_clk1_os,
       width = 9, height = 4, units = "in",
       device = "pdf")

# Assess EFS and OS by CLK1 ex 4 PSI in multivariate models and generate forest plots

add_model_cluster_efs <- fit_save_model(cluster_df %>% 
                                      dplyr::filter(extent_of_tumor_resection != "Unavailable"),
                                      # %>%
                                 #     dplyr::mutate(plot_group = fct_relevel(plot_group,
                                  #                                          c("Other high-grade glioma", "Atypical Teratoid Rhabdoid Tumor",
                                   #                                           "DIPG or DMG", "Ependymoma", "Mesenchymal tumor",
                                    #                                          "Other CNS embryonal tumor", "Low-grade glioma"))),
                              terms = "extent_of_tumor_resection+age_at_diagnosis_years+plot_group+CLK1_ex4_PSI",
                               file.path(results_dir, paste0( "cox_EFS_additive_terms_subtype_", safe_each, "_clk1_psi.RDS")),
                               "multivariate",
                               years_col = "EFS_years",
                               status_col = "EFS_status")

forest_cluster_clk1_efs <- plotForest(readRDS(file.path(results_dir, paste0( "cox_EFS_additive_terms_subtype_", safe_each, "_clk1_psi.RDS"))))

ggsave(file.path(plot_dir, paste0( "forest_add_EFS_", safe_each, "_histology_resection_clk1_psi.pdf")),
       forest_cluster_clk1_efs,
       width = 9, height = 4, units = "in",
       device = "pdf")

add_model_cluster_os <- fit_save_model(cluster_df %>% 
                                    dplyr::filter(!extent_of_tumor_resection %in% c("Not Reported", "Unavailable")),
                                    # %>%
                               #     dplyr::mutate(plot_group = fct_relevel(plot_group,
                                #                                            c("Other high-grade glioma", "Atypical Teratoid Rhabdoid Tumor",
                                 #                                             "DIPG or DMG", "Ependymoma", "Mesenchymal tumor",
                                  #                                            "Other CNS embryonal tumor", "Low-grade glioma")),                                 ),
                              terms = "extent_of_tumor_resection+age_at_diagnosis_years+plot_group+CLK1_ex4_PSI",
                               file.path(results_dir, paste0( "cox_OS_additive_terms_subtype_", safe_each, "_clk1_psi.RDS")),
                               "multivariate",
                               years_col = "OS_years",
                               status_col = "OS_status")

forest_cluster_clk1_os <- plotForest(readRDS(file.path(results_dir, paste0( "cox_OS_additive_terms_subtype_", safe_each, "_clk1_psi.RDS"))))

ggsave(file.path(plot_dir, paste0( "forest_add_OS_", each, "_histology_resection_clk1_psi.pdf")),
       forest_cluster_clk1_os,
       width = 9, height = 4, units = "in",
       device = "pdf")
}
Testing model: survival::Surv(OS_days, OS_status) ~ CLK1_TPM_group with kap.meier
Testing model: survival::Surv(EFS_days, EFS_status) ~ CLK1_TPM_group with kap.meier
Testing model: survival::Surv(OS_days, OS_status) ~ CLK1_PSI_group with kap.meier
Testing model: survival::Surv(EFS_days, EFS_status) ~ CLK1_PSI_group with kap.meier
Ignoring unknown labels:
• fill : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• fill : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• colour : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• fill : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• fill : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• colour : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• fill : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• fill : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• colour : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• fill : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• fill : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• colour : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Loglik converged before variable  2 ; coefficient may be infinite. 
`height` was translated to `width`.
Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Ran out of iterations and did not converge
Warning in scale_x_log10(labels = function(x) format(x, scientific = FALSE)) :
  log-10 transformation introduced infinite values.
`height` was translated to `width`.
Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Loglik converged before variable  2,6 ; coefficient may be infinite. 
`height` was translated to `width`.
Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Ran out of iterations and did not converge
Warning in scale_x_log10(labels = function(x) format(x, scientific = FALSE)) :
  log-10 transformation introduced infinite values.
`height` was translated to `width`.
Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Loglik converged before variable  2 ; coefficient may be infinite. 
`height` was translated to `width`.
Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Ran out of iterations and did not converge
Warning in scale_x_log10(labels = function(x) format(x, scientific = FALSE)) :
  log-10 transformation introduced infinite values.
`height` was translated to `width`.
Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Loglik converged before variable  2 ; coefficient may be infinite. 
`height` was translated to `width`.
Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Loglik converged before variable  1,4,5 ; coefficient may be infinite. 
`height` was translated to `width`.
Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_text()`).
Testing model: survival::Surv(OS_days, OS_status) ~ CLK1_TPM_group with kap.meier
Testing model: survival::Surv(EFS_days, EFS_status) ~ CLK1_TPM_group with kap.meier
Testing model: survival::Surv(OS_days, OS_status) ~ CLK1_PSI_group with kap.meier
Testing model: survival::Surv(EFS_days, EFS_status) ~ CLK1_PSI_group with kap.meier
Ignoring unknown labels:
• fill : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• fill : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• colour : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• fill : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• fill : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• colour : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• fill : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• fill : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• colour : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• fill : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• fill : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• colour : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Ran out of iterations and did not converge
Warning in scale_x_log10(labels = function(x) format(x, scientific = FALSE)) :
  log-10 transformation introduced infinite values.
`height` was translated to `width`.
Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Ran out of iterations and did not converge
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  one or more coefficients may be infinite
`height` was translated to `width`.
Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Ran out of iterations and did not converge
`height` was translated to `width`.
Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Loglik converged before variable  1,4,5,6,7,8,9,10 ; coefficient may be infinite. 
Warning in scale_x_log10(labels = function(x) format(x, scientific = FALSE)) :
  log-10 transformation introduced infinite values.
`height` was translated to `width`.
Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Loglik converged before variable  6,7,8,9 ; coefficient may be infinite. 
Warning in scale_x_log10(labels = function(x) format(x, scientific = FALSE)) :
  log-10 transformation introduced infinite values.
`height` was translated to `width`.
Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Loglik converged before variable  6,7,9 ; coefficient may be infinite. 
Warning in scale_x_log10(labels = function(x) format(x, scientific = FALSE)) :
  log-10 transformation introduced infinite values.
`height` was translated to `width`.
Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Loglik converged before variable  6,7,8,9 ; coefficient may be infinite. 
Warning in scale_x_log10(labels = function(x) format(x, scientific = FALSE)) :
  log-10 transformation introduced infinite values.
`height` was translated to `width`.
Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Loglik converged before variable  6,7,9 ; coefficient may be infinite. 
Warning in scale_x_log10(labels = function(x) format(x, scientific = FALSE)) :
  log-10 transformation introduced infinite values.
`height` was translated to `width`.
Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_text()`).
Testing model: survival::Surv(OS_days, OS_status) ~ CLK1_TPM_group with kap.meier
Testing model: survival::Surv(EFS_days, EFS_status) ~ CLK1_TPM_group with kap.meier
Testing model: survival::Surv(OS_days, OS_status) ~ CLK1_PSI_group with kap.meier
Testing model: survival::Surv(EFS_days, EFS_status) ~ CLK1_PSI_group with kap.meier
Ignoring unknown labels:
• fill : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• fill : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• colour : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• fill : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• fill : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• colour : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• fill : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• fill : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• colour : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• fill : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• fill : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• colour : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Ran out of iterations and did not converge
`height` was translated to `width`.
Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Ran out of iterations and did not converge
`height` was translated to `width`.
Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).
`height` was translated to `width`.
Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Ran out of iterations and did not converge
`height` was translated to `width`.
Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Ran out of iterations and did not converge
`height` was translated to `width`.
Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Ran out of iterations and did not converge
`height` was translated to `width`.
Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Ran out of iterations and did not converge
`height` was translated to `width`.
Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Ran out of iterations and did not converge
`height` was translated to `width`.
Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_text()`).
Testing model: survival::Surv(OS_days, OS_status) ~ CLK1_TPM_group with kap.meier
Testing model: survival::Surv(EFS_days, EFS_status) ~ CLK1_TPM_group with kap.meier
Testing model: survival::Surv(OS_days, OS_status) ~ CLK1_PSI_group with kap.meier
Testing model: survival::Surv(EFS_days, EFS_status) ~ CLK1_PSI_group with kap.meier
Ignoring unknown labels:
• fill : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• fill : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• colour : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• fill : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• fill : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• colour : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• fill : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• fill : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• colour : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• fill : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• fill : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• colour : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Ran out of iterations and did not converge
`height` was translated to `width`.
Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Ran out of iterations and did not converge
Warning in scale_x_log10(labels = function(x) format(x, scientific = FALSE)) :
  log-10 transformation introduced infinite values.
`height` was translated to `width`.
Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Ran out of iterations and did not converge
Warning in scale_x_log10(labels = function(x) format(x, scientific = FALSE)) :
  log-10 transformation introduced infinite values.
`height` was translated to `width`.
Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Ran out of iterations and did not converge
`height` was translated to `width`.
Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Ran out of iterations and did not converge
`height` was translated to `width`.
Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Ran out of iterations and did not converge
Warning in scale_x_log10(labels = function(x) format(x, scientific = FALSE)) :
  log-10 transformation introduced infinite values.
`height` was translated to `width`.
Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Ran out of iterations and did not converge
`height` was translated to `width`.
Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Ran out of iterations and did not converge
`height` was translated to `width`.
Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_text()`).
Testing model: survival::Surv(OS_days, OS_status) ~ CLK1_TPM_group with kap.meier
Testing model: survival::Surv(EFS_days, EFS_status) ~ CLK1_TPM_group with kap.meier
Testing model: survival::Surv(OS_days, OS_status) ~ CLK1_PSI_group with kap.meier
Testing model: survival::Surv(EFS_days, EFS_status) ~ CLK1_PSI_group with kap.meier
Ignoring unknown labels:
• fill : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• fill : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• colour : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• fill : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• fill : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• colour : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• fill : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• fill : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• colour : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• fill : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• fill : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• colour : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Loglik converged before variable  1,2,4,5,6,7,8 ; coefficient may be infinite. 
`height` was translated to `width`.
Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Ran out of iterations and did not converge
Warning in scale_x_log10(labels = function(x) format(x, scientific = FALSE)) :
  log-10 transformation introduced infinite values.
`height` was translated to `width`.
Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Loglik converged before variable  1,2,4,5,6,7,8 ; coefficient may be infinite. 
`height` was translated to `width`.
Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Ran out of iterations and did not converge
Warning in scale_x_log10(labels = function(x) format(x, scientific = FALSE)) :
  log-10 transformation introduced infinite values.
`height` was translated to `width`.
Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Ran out of iterations and did not converge
`height` was translated to `width`.
Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Ran out of iterations and did not converge
`height` was translated to `width`.
Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Ran out of iterations and did not converge
`height` was translated to `width`.
Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Ran out of iterations and did not converge
`height` was translated to `width`.
Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_text()`).
Testing model: survival::Surv(OS_days, OS_status) ~ CLK1_TPM_group with kap.meier
Testing model: survival::Surv(EFS_days, EFS_status) ~ CLK1_TPM_group with kap.meier
Testing model: survival::Surv(OS_days, OS_status) ~ CLK1_PSI_group with kap.meier
Testing model: survival::Surv(EFS_days, EFS_status) ~ CLK1_PSI_group with kap.meier
Ignoring unknown labels:
• fill : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• fill : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• colour : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• fill : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• fill : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• colour : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• fill : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• fill : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• colour : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• fill : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• fill : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• colour : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Ran out of iterations and did not converge
Warning in scale_x_log10(labels = function(x) format(x, scientific = FALSE)) :
  log-10 transformation introduced infinite values.
`height` was translated to `width`.
Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Ran out of iterations and did not converge
Warning in scale_x_log10(labels = function(x) format(x, scientific = FALSE)) :
  log-10 transformation introduced infinite values.
`height` was translated to `width`.
Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Ran out of iterations and did not converge
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  one or more coefficients may be infinite
`height` was translated to `width`.
Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Ran out of iterations and did not converge
`height` was translated to `width`.
Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Loglik converged before variable  1,2,3,4,5,7,8,9,10,11 ; coefficient may be infinite. 
Warning in scale_x_log10(labels = function(x) format(x, scientific = FALSE)) :
  log-10 transformation introduced infinite values.
`height` was translated to `width`.
Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Ran out of iterations and did not converge
`height` was translated to `width`.
Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Ran out of iterations and did not converge
Warning in scale_x_log10(labels = function(x) format(x, scientific = FALSE)) :
  log-10 transformation introduced infinite values.
`height` was translated to `width`.
Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_text()`).
Testing model: survival::Surv(OS_days, OS_status) ~ CLK1_TPM_group with kap.meier
Testing model: survival::Surv(EFS_days, EFS_status) ~ CLK1_TPM_group with kap.meier
Testing model: survival::Surv(OS_days, OS_status) ~ CLK1_PSI_group with kap.meier
Testing model: survival::Surv(EFS_days, EFS_status) ~ CLK1_PSI_group with kap.meier
Ignoring unknown labels:
• fill : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• fill : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• colour : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• fill : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• fill : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• colour : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• fill : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• fill : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• colour : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• fill : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• fill : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• colour : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
`height` was translated to `width`.
Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Ran out of iterations and did not converge
`height` was translated to `width`.
Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).
`height` was translated to `width`.
Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Loglik converged before variable  5,6,7 ; coefficient may be infinite. 
`height` was translated to `width`.
Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).
`height` was translated to `width`.
Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Loglik converged before variable  5,6,7 ; coefficient may be infinite. 
`height` was translated to `width`.
Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_text()`).
`height` was translated to `width`.
Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Loglik converged before variable  5,6,7 ; coefficient may be infinite. 
`height` was translated to `width`.
Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_text()`).
Testing model: survival::Surv(OS_days, OS_status) ~ CLK1_TPM_group with kap.meier
Testing model: survival::Surv(EFS_days, EFS_status) ~ CLK1_TPM_group with kap.meier
Testing model: survival::Surv(OS_days, OS_status) ~ CLK1_PSI_group with kap.meier
Testing model: survival::Surv(EFS_days, EFS_status) ~ CLK1_PSI_group with kap.meier
Ignoring unknown labels:
• fill : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• fill : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• colour : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• fill : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• fill : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• colour : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• fill : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• fill : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• colour : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• fill : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• fill : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• colour : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Loglik converged before variable  1,2 ; coefficient may be infinite. 
`height` was translated to `width`.
Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Ran out of iterations and did not converge
`height` was translated to `width`.
Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Loglik converged before variable  4 ; coefficient may be infinite. 
`height` was translated to `width`.
Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Loglik converged before variable  1,2,4,5 ; coefficient may be infinite. 
`height` was translated to `width`.
Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Loglik converged before variable  4,6,8 ; coefficient may be infinite. 
Warning in scale_x_log10(labels = function(x) format(x, scientific = FALSE)) :
  log-10 transformation introduced infinite values.
`height` was translated to `width`.
Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Ran out of iterations and did not converge
Warning in scale_x_log10(labels = function(x) format(x, scientific = FALSE)) :
  log-10 transformation introduced infinite values.
`height` was translated to `width`.
Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Loglik converged before variable  4,6,8 ; coefficient may be infinite. 
Warning in scale_x_log10(labels = function(x) format(x, scientific = FALSE)) :
  log-10 transformation introduced infinite values.
`height` was translated to `width`.
Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Ran out of iterations and did not converge
Warning in scale_x_log10(labels = function(x) format(x, scientific = FALSE)) :
  log-10 transformation introduced infinite values.
`height` was translated to `width`.
Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_text()`).
Testing model: survival::Surv(OS_days, OS_status) ~ CLK1_TPM_group with kap.meier
Testing model: survival::Surv(EFS_days, EFS_status) ~ CLK1_TPM_group with kap.meier
Testing model: survival::Surv(OS_days, OS_status) ~ CLK1_PSI_group with kap.meier
Testing model: survival::Surv(EFS_days, EFS_status) ~ CLK1_PSI_group with kap.meier
Ignoring unknown labels:
• fill : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• fill : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• colour : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• fill : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• fill : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• colour : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• fill : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• fill : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• colour : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• fill : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• fill : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• colour : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Loglik converged before variable  4,5,7,8 ; coefficient may be infinite. 
Warning in scale_x_log10(labels = function(x) format(x, scientific = FALSE)) :
  log-10 transformation introduced infinite values.
`height` was translated to `width`.
Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Ran out of iterations and did not converge
Warning in scale_x_log10(labels = function(x) format(x, scientific = FALSE)) :
  log-10 transformation introduced infinite values.
`height` was translated to `width`.
Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Loglik converged before variable  5,6 ; coefficient may be infinite. 
`height` was translated to `width`.
Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Ran out of iterations and did not converge
Warning in scale_x_log10(labels = function(x) format(x, scientific = FALSE)) :
  log-10 transformation introduced infinite values.
`height` was translated to `width`.
Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Ran out of iterations and did not converge
Warning in scale_x_log10(labels = function(x) format(x, scientific = FALSE)) :
  log-10 transformation introduced infinite values.
`height` was translated to `width`.
Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Ran out of iterations and did not converge
Warning in scale_x_log10(labels = function(x) format(x, scientific = FALSE)) :
  log-10 transformation introduced infinite values.
`height` was translated to `width`.
Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Ran out of iterations and did not converge
Warning in scale_x_log10(labels = function(x) format(x, scientific = FALSE)) :
  log-10 transformation introduced infinite values.
`height` was translated to `width`.
Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Ran out of iterations and did not converge
Warning in scale_x_log10(labels = function(x) format(x, scientific = FALSE)) :
  log-10 transformation introduced infinite values.
`height` was translated to `width`.
Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_text()`).
Testing model: survival::Surv(OS_days, OS_status) ~ CLK1_TPM_group with kap.meier
Testing model: survival::Surv(EFS_days, EFS_status) ~ CLK1_TPM_group with kap.meier
Testing model: survival::Surv(OS_days, OS_status) ~ CLK1_PSI_group with kap.meier
Testing model: survival::Surv(EFS_days, EFS_status) ~ CLK1_PSI_group with kap.meier
Ignoring unknown labels:
• fill : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• fill : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• colour : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• fill : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• fill : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• colour : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• fill : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• fill : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• colour : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• fill : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• fill : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Ignoring unknown labels:
• colour : ""
Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Loglik converged before variable  4 ; coefficient may be infinite. 
`height` was translated to `width`.
Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Ran out of iterations and did not converge
`height` was translated to `width`.
Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Ran out of iterations and did not converge
Warning in scale_x_log10(labels = function(x) format(x, scientific = FALSE)) :
  log-10 transformation introduced infinite values.
`height` was translated to `width`.
Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Loglik converged before variable  1,2,4,6 ; coefficient may be infinite. 
Warning in scale_x_log10(labels = function(x) format(x, scientific = FALSE)) :
  log-10 transformation introduced infinite values.
`height` was translated to `width`.
Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Loglik converged before variable  4,6 ; coefficient may be infinite. 
Warning in scale_x_log10(labels = function(x) format(x, scientific = FALSE)) :
  log-10 transformation introduced infinite values.
`height` was translated to `width`.
Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Loglik converged before variable  4,6 ; coefficient may be infinite. 
Warning in scale_x_log10(labels = function(x) format(x, scientific = FALSE)) :
  log-10 transformation introduced infinite values.
`height` was translated to `width`.
Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Loglik converged before variable  4,6 ; coefficient may be infinite. 
Warning in scale_x_log10(labels = function(x) format(x, scientific = FALSE)) :
  log-10 transformation introduced infinite values.
`height` was translated to `width`.
Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Loglik converged before variable  4,6 ; coefficient may be infinite. 
Warning in scale_x_log10(labels = function(x) format(x, scientific = FALSE)) :
  log-10 transformation introduced infinite values.
`height` was translated to `width`.
Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_text()`).

Print session info

sessionInfo()
R version 4.4.0 (2024-04-24)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 22.04.4 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8     LC_MONETARY=en_US.UTF-8   
 [6] LC_MESSAGES=en_US.UTF-8    LC_PAPER=en_US.UTF-8       LC_NAME=C                  LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

time zone: Etc/UTC
tzcode source: system (glibc)

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] gtools_3.9.5    survminer_0.4.9 patchwork_1.2.0 ggpubr_0.6.0    survival_3.7-0  lubridate_1.9.4 forcats_1.0.1   stringr_1.6.0   dplyr_1.1.4    
[10] purrr_1.2.0     readr_2.1.6     tidyr_1.3.1     tibble_3.3.0    ggplot2_4.0.1   tidyverse_2.0.0

loaded via a namespace (and not attached):
 [1] tidyselect_1.2.1   farver_2.1.2       S7_0.2.1           fastmap_1.2.0      digest_0.6.39      timechange_0.3.0   lifecycle_1.0.4   
 [8] magrittr_2.0.4     compiler_4.4.0     rlang_1.1.6        sass_0.4.10        tools_4.4.0        yaml_2.3.10        data.table_1.17.8 
[15] knitr_1.50         ggsignif_0.6.4     labeling_0.4.3     bit_4.6.0          xml2_1.5.0         RColorBrewer_1.1-3 abind_1.4-5       
[22] withr_3.0.2        grid_4.4.0         xtable_1.8-4       colorspace_2.1-2   scales_1.4.0       cli_3.6.5          rmarkdown_2.30    
[29] crayon_1.5.3       ragg_1.5.0         generics_0.1.4     rstudioapi_0.17.1  km.ci_0.5-6        tzdb_0.5.0         commonmark_2.0.0  
[36] cachem_1.1.0       splines_4.4.0      survMisc_0.5.6     vctrs_0.6.5        Matrix_1.7-4       jsonlite_2.0.0     carData_3.0-5     
[43] car_3.1-2          colorblindr_0.1.0  hms_1.1.4          bit64_4.6.0-1      rstatix_0.7.2      systemfonts_1.3.1  jquerylib_0.1.4   
[50] glue_1.8.0         ggtext_0.1.2       cowplot_1.1.3      stringi_1.8.7      gtable_0.3.6       pillar_1.11.1      htmltools_0.5.8.1 
[57] R6_2.6.1           KMsurv_0.1-5       textshaping_1.0.4  rprojroot_2.1.1    vroom_1.6.6        evaluate_1.0.5     lattice_0.22-7    
[64] markdown_1.13      backports_1.5.0    gridtext_0.1.5     broom_1.0.10       bslib_0.9.0        Rcpp_1.1.0         gridExtra_2.3     
[71] xfun_0.54          zoo_1.8-12         pkgconfig_2.0.3   
---
title: "Run splicing cluster assignment, splicing burden, gsva, CLK1"
output: 
  html_notebook:
    toc: TRUE
    toc_float: TRUE
author: Ryan Corbett, adapted by Jo Lynne Rokita for CLK1
date: 2024
params:
  plot_ci: TRUE
---

**Purpose:** 

Runs survival analysis models using splicing cluster assignment and 1) single exon splicing burden index (SBI) 2) KEGG Spliceosome GSVA scores or 3) CLK1 exon 4 TPM as a predictor

## Usage 

Uses a wrapper function (`survival_analysis`) from utils folder. 

## Setup

#### Packages and functions

Load packages, set directory paths and call setup script

```{r}
library(tidyverse)
library(survival)
library(ggpubr)
library(ggplot2)
library(patchwork)

root_dir <- rprojroot::find_root(rprojroot::has_dir(".git"))

data_dir <- file.path(root_dir, "data")
analysis_dir <- file.path(root_dir, "analyses", "survival")
input_dir <- file.path(analysis_dir, "results")
results_dir <- file.path(analysis_dir, "results")
plot_dir <- file.path(analysis_dir, "plots")

# If the input and results directories do not exist, create it
if (!dir.exists(results_dir)) {
  dir.create(results_dir, recursive = TRUE)
}

source(file.path(analysis_dir, "util", "survival_models.R"))
```

Set metadata and cluster assignment file paths

```{r set paths}
metadata_file <- file.path(input_dir, "splicing_indices_with_survival.tsv")

cluster_file <- file.path(root_dir, "analyses",
                          "sample-psi-clustering", "results",
                          "sample-cluster-metadata-top-5000-events-stranded.tsv")

kegg_scores_stranded_file <- file.path(root_dir, "analyses",
                          "sample-psi-clustering", "results",
                          "gsva_output_stranded.tsv")

tpm_file <- file.path(data_dir, "rna-isoform-expression-rsem-tpm.rds")
clk1_psi_file <- file.path(root_dir, 
                           "analyses", 
                           "CLK1-splicing_correlations", 
                           "results", 
                           "clk1-exon4-psi.tsv")
```

Wrangle data 
Add cluster assignment and spliceosome gsva scores to `metadata` and define column `lgg_group` (LGG or non_LGG)

```{r}
metadata <- read_tsv(metadata_file)

clusters <- read_tsv(cluster_file) %>%
  dplyr::rename(Kids_First_Biospecimen_ID = sample_id)

clk1_psi <- read_tsv(clk1_psi_file) %>%
  dplyr::rename(CLK1_ex4_PSI = PSI) %>%
  select(-plot_group)

gsva_scores <- read_tsv(kegg_scores_stranded_file) %>%
  dplyr::filter(geneset == "KEGG_SPLICEOSOME") %>%
  dplyr::rename(spliceosome_gsva_score = score)

all_clk4_transcr_counts <- readRDS(tpm_file) %>%
  filter(grepl("^CLK1", gene_symbol)) %>%
  mutate(
    transcript_id = case_when(
      transcript_id %in% c("ENST00000321356.9", "ENST00000434813.3", "ENST00000409403.6") ~ "Exon 4",
#      transcript_id == "ENST00000321356.9" ~ "Exon 4",
      TRUE ~ "Other"
    )
  ) %>%
  group_by(transcript_id) %>%
  summarise(across(starts_with("BS"), sum, na.rm = TRUE)) %>%
  pivot_longer(cols = -transcript_id, names_to = "Kids_First_Biospecimen_ID", values_to = "CLK1_Ex4_TPM") %>%
  filter(transcript_id == "Exon 4") %>%
  inner_join(clusters, by = "Kids_First_Biospecimen_ID") %>%
  left_join(clk1_psi)

# how many clusters?
n_clust <- length(unique(clusters$cluster))

metadata <- metadata %>%
  right_join(all_clk4_transcr_counts %>% dplyr::select(Kids_First_Biospecimen_ID,
                                       cluster, CLK1_Ex4_TPM, CLK1_ex4_PSI)) %>%
  left_join(gsva_scores %>% dplyr::select(sample_id,
                                          spliceosome_gsva_score),
            by = c("Kids_First_Biospecimen_ID" = "sample_id")) %>% 
  dplyr::mutate(cluster = glue::glue("Cluster {cluster}")) %>%
  dplyr::mutate(cluster = fct_relevel(cluster,
                                               paste0("Cluster ", 1:n_clust))) %>%
  dplyr::mutate(lgg_group = case_when(
    plot_group == "Low-grade glioma" ~ "LGG",
    TRUE ~ "non-LGG"
  )) %>%
  dplyr::mutate(SBI = SI_Total * 10) %>%
  dplyr::mutate(age_at_diagnosis_years = age_at_diagnosis_days/365.25)
```

Generate coxph models including extent of tumor resection, lgg group, cluster assignment, SBI, and CLK1 exon 4 TPM as covariates

```{r}
add_model_os <- fit_save_model(metadata[!metadata$extent_of_tumor_resection %in% c("Not Reported", "Unavailable"),],
                              terms = "extent_of_tumor_resection+lgg_group+cluster+age_at_diagnosis_years+SBI+CLK1_Ex4_TPM",
                               file.path(results_dir, "cox_OS_additive_terms_resection_lgg_group_cluster_SBI_CLK1_Ex4_TPM.RDS"),
                               "multivariate",
                               years_col = "OS_years",
                               status_col = "OS_status")

forest_os <- plotForest(readRDS(file.path(results_dir, "cox_OS_additive_terms_resection_lgg_group_cluster_SBI_CLK1_Ex4_TPM.RDS")))

forest_os

ggsave(file.path(plot_dir, "forest_add_OS_resection_lgg_group_cluster_assignment_SBI_CLK1_Ex4_TPM.pdf"),
       forest_os,
       width = 10, height = 6, units = "in",
       device = "pdf")

add_model_efs <- fit_save_model(metadata[!metadata$extent_of_tumor_resection %in% c("Not Reported", "Unavailable"),],
                              terms = "extent_of_tumor_resection+lgg_group+cluster+age_at_diagnosis_years+SBI+CLK1_Ex4_TPM",
                               file.path(results_dir, "cox_EFS_additive_terms_resection_lgg_group_cluster_SBI_CLK1_Ex4_TPM.RDS"),
                               "multivariate",
                               years_col = "EFS_years",
                               status_col = "EFS_status")

forest_efs <- plotForest(readRDS(file.path(results_dir, "cox_EFS_additive_terms_resection_lgg_group_cluster_SBI_CLK1_Ex4_TPM.RDS")))

forest_efs

ggsave(file.path(plot_dir, "forest_add_EFS_resection_lgg_group_cluster_assignment_SBI_CLK1_Ex4_TPM.pdf"),
       forest_efs,
       width = 10, height = 6, units = "in",
       device = "pdf")
```
repeat analysis with CLK1 exon 4 TPM alone

```{r}
add_model_os <- fit_save_model(metadata[!metadata$extent_of_tumor_resection %in% c("Not Reported", "Unavailable"),],
                              terms = "extent_of_tumor_resection+lgg_group+cluster+age_at_diagnosis_years+CLK1_Ex4_TPM",
                               file.path(results_dir, "cox_OS_additive_terms_resection_lgg_group_cluster_CLK1_Ex4_TPM.RDS"),
                               "multivariate",
                               years_col = "OS_years",
                               status_col = "OS_status")

forest_os <- plotForest(readRDS(file.path(results_dir, "cox_OS_additive_terms_resection_lgg_group_cluster_CLK1_Ex4_TPM.RDS")))

forest_os

ggsave(file.path(plot_dir, "forest_add_OS_resection_lgg_group_cluster_assignment_CLK1_Ex4_TPM.pdf"),
       forest_os,
       width = 10, height = 6, units = "in",
       device = "pdf")


add_model_efs <- fit_save_model(metadata[!metadata$extent_of_tumor_resection %in% c("Not Reported", "Unavailable"),],
                              terms = "extent_of_tumor_resection+lgg_group+cluster+age_at_diagnosis_years+CLK1_Ex4_TPM",
                               file.path(results_dir, "cox_EFS_additive_terms_resection_lgg_group_cluster_CLK1_Ex4_TPM.RDS"),
                               "multivariate",
                               years_col = "EFS_years",
                               status_col = "EFS_status")

forest_efs <- plotForest(readRDS(file.path(results_dir, "cox_EFS_additive_terms_resection_lgg_group_cluster_CLK1_Ex4_TPM.RDS")))

forest_efs

ggsave(file.path(plot_dir, "forest_add_EFS_resection_lgg_group_cluster_assignment_CLK1_Ex4_TPM.pdf"),
       forest_efs,
       width = 10, height = 6, units = "in",
       device = "pdf")


```


repeat analysis with CLK1 exon 4 PSI

```{r}
add_model_os <- fit_save_model(metadata[!metadata$extent_of_tumor_resection %in% c("Not Reported", "Unavailable"),],
                              terms = "extent_of_tumor_resection+lgg_group+cluster+age_at_diagnosis_years+CLK1_ex4_PSI",
                               file.path(results_dir, "cox_OS_additive_terms_resection_lgg_group_cluster_CLK1_ex4_PSI.RDS"),
                               "multivariate",
                               years_col = "OS_years",
                               status_col = "OS_status")

forest_os <- plotForest(readRDS(file.path(results_dir, "cox_OS_additive_terms_resection_lgg_group_cluster_CLK1_ex4_PSI.RDS")))

forest_os

ggsave(file.path(plot_dir, "forest_add_OS_resection_lgg_group_cluster_assignment_CLK1_ex4_PSI.pdf"),
       forest_os,
       width = 10, height = 6, units = "in",
       device = "pdf")

add_model_efs <- fit_save_model(metadata[!metadata$extent_of_tumor_resection %in% c("Not Reported", "Unavailable"),],
                              terms = "extent_of_tumor_resection+lgg_group+cluster+age_at_diagnosis_years+CLK1_ex4_PSI",
                               file.path(results_dir, "cox_EFS_additive_terms_resection_lgg_group_cluster_CLK1_ex4_PSI.RDS"),
                               "multivariate",
                               years_col = "EFS_years",
                               status_col = "EFS_status")

forest_efs <- plotForest(readRDS(file.path(results_dir, "cox_EFS_additive_terms_resection_lgg_group_cluster_CLK1_ex4_PSI.RDS")))

forest_efs

ggsave(file.path(plot_dir, "forest_add_EFS_resection_lgg_group_cluster_assignment_CLK1_ex4_PSI.pdf"),
       forest_efs,
       width = 10, height = 6, units = "in",
       device = "pdf")
```


Interaction with GSVA, SBI, CLK1

```{r Interaction models with GSVA, SBI, CLK1}
models <- c("spliceosome_gsva_score", "SBI", "CLK1_Ex4_TPM", "CLK1_ex4_PSI")
# by cluster
for (each in models) {
  int_model_efs <- fit_save_model(metadata[!metadata$extent_of_tumor_resection %in% c("Not Reported", "Unavailable"),],
                                terms = paste0("extent_of_tumor_resection+lgg_group+cluster*", each, "+age_at_diagnosis_years"),
                                 file.path(results_dir, paste0("cox_EFS_interaction_terms_resection_lgg_group_cluster_", each, ".RDS")),
                                 "multivariate",
                                 years_col = "EFS_years",
                                 status_col = "EFS_status")
  
  int_forest_efs <- plotForest(readRDS(file.path(results_dir, paste0("cox_EFS_interaction_terms_resection_lgg_group_cluster_", each, ".RDS"))))
  
  int_forest_efs
  
  ggsave(file.path(plot_dir, paste0("forest_int_EFS_resection_lgg_group_cluster_assignment_", each, ".pdf")),
         int_forest_efs,
         width = 10, height = 6, units = "in",
         device = "pdf")

  int_model_os <- fit_save_model(metadata[!metadata$extent_of_tumor_resection %in% c("Not Reported", "Unavailable"),],
                                terms = paste0("extent_of_tumor_resection+lgg_group+cluster*", each, "+age_at_diagnosis_years"),
                                 file.path(results_dir, paste0("cox_OS_interaction_terms_resection_lgg_group_cluster_", each, ".RDS")),
                                 "multivariate",
                                 years_col = "OS_years",
                                 status_col = "OS_status")
  
  int_forest_os <- plotForest(readRDS(file.path(results_dir, paste0("cox_OS_interaction_terms_resection_lgg_group_cluster_", each, ".RDS"))))
  
  int_forest_os
  
  ggsave(file.path(plot_dir, paste0("forest_int_OS_resection_lgg_group_cluster_assignment_", each, ".pdf")),
         int_forest_os,
         width = 10, height = 6, units = "in",
         device = "pdf")
}

## clk1 x age
  int_model_efs <- fit_save_model(metadata[!metadata$extent_of_tumor_resection %in% c("Not Reported", "Unavailable"),],
                                terms = paste0("extent_of_tumor_resection+lgg_group+cluster+CLK1_Ex4_TPM*age_at_diagnosis_years"),
                                 file.path(results_dir, paste0("cox_EFS_interaction_terms_resection_lgg_group_cluster_CLK1_Ex4_TPM_age.RDS")),
                                 "multivariate",
                                 years_col = "EFS_years",
                                 status_col = "EFS_status")
  
  int_forest_efs <- plotForest(readRDS(file.path(results_dir, paste0("cox_EFS_interaction_terms_resection_lgg_group_cluster_CLK1_Ex4_TPM_age.RDS"))))
  
  int_forest_efs
  
  ggsave(file.path(plot_dir, paste0("forest_int_EFS_resection_lgg_group_cluster_clk1_age.pdf")),
         int_forest_efs,
         width = 10, height = 6, units = "in",
         device = "pdf")

  int_model_os <- fit_save_model(metadata[!metadata$extent_of_tumor_resection %in% c("Not Reported", "Unavailable"),],
                                terms = paste0("extent_of_tumor_resection+lgg_group+cluster+CLK1_Ex4_TPM*age_at_diagnosis_years"),
                                 file.path(results_dir, paste0("cox_OS_interaction_terms_resection_lgg_group_cluster_clk1_age.RDS")),
                                 "multivariate",
                                 years_col = "OS_years",
                                 status_col = "OS_status")
  
  int_forest_os <- plotForest(readRDS(file.path(results_dir, paste0("cox_OS_interaction_terms_resection_lgg_group_cluster_clk1_age.RDS"))))
  
  int_forest_os
  
  ggsave(file.path(plot_dir, paste0("forest_int_OS_resection_lgg_group_cluster_clk1_age.pdf")),
         int_forest_os,
         width = 10, height = 6, units = "in",
         device = "pdf")

models2 <- c("SBI", "CLK1_Ex4_TPM")
for (each in models2) {
  #### by spliceosome_gsva_score
  int_model_efs <- fit_save_model(metadata[!metadata$extent_of_tumor_resection %in% c("Not Reported", "Unavailable"),],
                                terms = paste0("extent_of_tumor_resection+lgg_group+cluster+spliceosome_gsva_score*", each, "+age_at_diagnosis_years"),
                                 file.path(results_dir, paste0("cox_EFS_interaction_terms_resection_lgg_group_cluster_spliceosome_gsva_score_", each, ".RDS")),
                                 "multivariate",
                                 years_col = "EFS_years",
                                 status_col = "EFS_status")
  
  int_forest_efs <- plotForest(readRDS(file.path(results_dir, paste0("cox_EFS_interaction_terms_resection_lgg_group_cluster_spliceosome_gsva_score_", each, ".RDS"))))
  
  int_forest_efs
  
  ggsave(file.path(plot_dir, paste0("cox_EFS_interaction_terms_resection_lgg_group_cluster_spliceosome_gsva_score_", each, ".pdf")),
         int_forest_efs,
         width = 10, height = 6, units = "in",
         device = "pdf")
}
  


add_model_efs <- fit_save_model(metadata[!metadata$extent_of_tumor_resection %in% c("Not Reported", "Unavailable"),],
                              terms = "extent_of_tumor_resection+lgg_group+cluster+age_at_diagnosis_years+spliceosome_gsva_score+CLK1_Ex4_TPM",
                               file.path(results_dir, "cox_EFS_additive_terms_resection_lgg_group_cluster_spliceosome_score_CLK1_Ex4_TPM.RDS"),
                               "multivariate",
                               years_col = "EFS_years",
                               status_col = "EFS_status")

forest_efs <- plotForest(readRDS(file.path(results_dir, "cox_EFS_additive_terms_resection_lgg_group_cluster_spliceosome_score_CLK1_Ex4_TPM.RDS")))

forest_efs

ggsave(file.path(plot_dir, "forest_add_EFS_resection_lgg_group_cluster_assignment_spliceosome_score_CLK1_Ex4_TPM.pdf"),
       forest_efs,
       width = 10, height = 6, units = "in",
       device = "pdf")


add_model_os <- fit_save_model(metadata[!metadata$extent_of_tumor_resection %in% c("Not Reported", "Unavailable"),],
                              terms = "extent_of_tumor_resection+lgg_group+cluster+age_at_diagnosis_years+spliceosome_gsva_score+CLK1_Ex4_TPM",
                               file.path(results_dir, "cox_OS_additive_terms_resection_lgg_group_cluster_spliceosome_score_CLK1_Ex4_TPM.RDS"),
                               "multivariate",
                               years_col = "EFS_years",
                               status_col = "EFS_status")

forest_os <- plotForest(readRDS(file.path(results_dir, "cox_OS_additive_terms_resection_lgg_group_cluster_spliceosome_score_CLK1_Ex4_TPM.RDS")))

forest_os

ggsave(file.path(plot_dir, "forest_add_OS_resection_lgg_group_cluster_assignment_spliceosome_score_CLK1_Ex4_TPM.pdf"),
       forest_efs,
       width = 10, height = 6, units = "in",
       device = "pdf")


```



Filter for clusters

```{r}
cluster_list <- unique(metadata$cluster)

for (each in cluster_list) {
cluster_df <- metadata %>%
  dplyr::filter(cluster == each,
                !is.na(EFS_days)) %>%
  dplyr::mutate(CLK1_TPM_group = case_when(
      CLK1_Ex4_TPM > summary(CLK1_Ex4_TPM)["3rd Qu."] ~ "High CLK1 TPM",
      CLK1_Ex4_TPM < summary(CLK1_Ex4_TPM)["1st Qu."] ~ "Low CLK1 TPM",
      TRUE ~ NA_character_),
      CLK1_PSI_group = case_when(CLK1_ex4_PSI > summary(CLK1_ex4_PSI)["3rd Qu."] ~ "High CLK1 PSI",
      CLK1_ex4_PSI < summary(CLK1_ex4_PSI)["1st Qu."] ~ "Low CLK1 PSI",
      TRUE ~ NA_character_
    )) %>%
  dplyr::mutate(CLK1_TPM_group = fct_relevel(CLK1_TPM_group,
                                                 c("Low CLK1 TPM", "High CLK1 TPM")),
                CLK1_PSI_group = fct_relevel(CLK1_PSI_group,
                                                 c("Low CLK1 PSI", "High CLK1 PSI")))

safe_each <- gsub("[^A-Za-z0-9_-]+", "_", each)

# Generate KM models with `CLK1_TPM_group` as covariate
# Generate kaplan meier survival models for OS and EFS, and save outputs
cluster_clk_tpm_kap_os <- survival_analysis(
  metadata  = cluster_df %>% 
    dplyr::filter(!is.na(CLK1_TPM_group)),
  ind_var = "CLK1_TPM_group",
  test = "kap.meier",
  metadata_sample_col = "Kids_First_Biospecimen_ID",
  days_col = "OS_days",
  status_col = "OS_status"
)

readr::write_rds(cluster_clk_tpm_kap_os,
                 file.path(results_dir, paste0( "logrank_", safe_each, "_OS_clk1_tpm_group.RDS")))

cluster_clk_tpm_kap_efs <- survival_analysis(
  metadata  = cluster_df %>% 
    dplyr::filter(!is.na(CLK1_TPM_group)),
  ind_var = "CLK1_TPM_group",
  test = "kap.meier",
  metadata_sample_col = "Kids_First_Biospecimen_ID",
  days_col = "EFS_days",
  status_col = "EFS_status"
)

readr::write_rds(cluster_clk_tpm_kap_efs,
                 file.path(results_dir, paste0( "logrank_", safe_each, "_EFS_clk1_tpm_group.RDS")))

# Generate KM models with `CLK1_PSI_group` as covariate
# Generate kaplan meier survival models for OS and EFS, and save outputs
cluster_clk_psi_kap_os <- survival_analysis(
  metadata  = cluster_df %>% 
    dplyr::filter(!is.na(CLK1_PSI_group)),
  ind_var = "CLK1_PSI_group",
  test = "kap.meier",
  metadata_sample_col = "Kids_First_Biospecimen_ID",
  days_col = "OS_days",
  status_col = "OS_status"
)

readr::write_rds(cluster_clk_psi_kap_os,
                 file.path(results_dir, paste0( "logrank_", safe_each, "_OS_clk1_psi_group.RDS")))

cluster_clk_psi_kap_efs <- survival_analysis(
  metadata  = cluster_df %>% 
    dplyr::filter(!is.na(CLK1_PSI_group)),
  ind_var = "CLK1_PSI_group",
  test = "kap.meier",
  metadata_sample_col = "Kids_First_Biospecimen_ID",
  days_col = "EFS_days",
  status_col = "EFS_status"
)

readr::write_rds(cluster_clk_psi_kap_efs,
                 file.path(results_dir, paste0( "logrank_", safe_each, "_EFS_clk1_psi_group.RDS")))

# Generate cluster KM SI_group plots

km_cluster_clk_tpm_os_plot <- plotKM(model = cluster_clk_tpm_kap_os,
                    variable = "CLK1_TPM_group",
                    combined = F, 
                    title = paste0(each, ", overall survival"),
                    p_pos = "topright")

ggsave(file.path(plot_dir, paste0("km_", safe_each, "_OS_clk1_tpm_group.pdf")),
       km_cluster_clk_tpm_os_plot,
       width = 8, height = 5, units = "in",
       device = "pdf")

km_cluster_clk1_tpm_efs_plot <- plotKM(model = cluster_clk_tpm_kap_efs,
                    variable = "CLK1_TPM_group",
                    combined = F, 
                    title = paste0(each, ", event-free survival"),
                    p_pos = "topright")

ggsave(file.path(plot_dir, paste0( "km_", each, "_EFS_clk1_tpm_group.pdf")), 
       km_cluster_clk1_tpm_efs_plot,
       width = 8, height = 5, units = "in",
       device = "pdf")

km_cluster_clk1_psi_os_plot <- plotKM(model = cluster_clk_psi_kap_os,
                    variable = "CLK1_PSI_group",
                    combined = F, 
                    title = paste0(each, ", overall survival"),
                    p_pos = "topright")

ggsave(file.path(plot_dir, paste0( "km_", each, "_OS_clk1_psi_group.pdf")),
       km_cluster_clk1_psi_os_plot,
       width = 8, height = 5, units = "in",
       device = "pdf")

km_cluster_clk1_psi_efs_plot <- plotKM(model = cluster_clk_psi_kap_efs,
                    variable = "CLK1_PSI_group",
                    combined = F, 
                    title = paste0(each, ", event-free survival"),
                    p_pos = "topright")

ggsave(file.path(plot_dir, paste0("km_", each, "_EFS_clk1_psi_group.pdf")), 
       km_cluster_clk1_psi_efs_plot,
       width = 8, height = 5, units = "in",
       device = "pdf")

# Assess EFS and OS by CLK1 TPM group in multivariate models and generate forest plots

add_model_cluster_efs <- fit_save_model(cluster_df %>% 
                                      dplyr::filter(extent_of_tumor_resection != "Unavailable",
                                                    CLK1_TPM_group %in% c("High CLK1 TPM", "Low CLK1 TPM")
                                                    ),
                                      # %>%
                                     # dplyr::mutate(plot_group = fct_relevel(plot_group,
                                      #                                      c("Other high-grade glioma", "Atypical Teratoid Rhabdoid Tumor",
                                       #                                       "DIPG or DMG", "Ependymoma", "Mesenchymal tumor",
                                        #                                      "Other CNS embryonal tumor", "Low-grade glioma"))),
                              terms = "extent_of_tumor_resection+age_at_diagnosis_years+plot_group+CLK1_TPM_group",
                               file.path(results_dir, paste0( "cox_EFS_additive_terms_subtype_", safe_each, "_clk1_tpm_group.RDS")),
                               "multivariate",
                               years_col = "EFS_years",
                               status_col = "EFS_status")

forest_cluster_clk1_efs <- plotForest(readRDS(file.path(results_dir, paste0("cox_EFS_additive_terms_subtype_", safe_each, "_clk1_tpm_group.RDS"))))

ggsave(file.path(plot_dir, paste0("forest_add_EFS_", safe_each, "_histology_resection_clk1_tpm_group.pdf")),
       forest_cluster_clk1_efs,
       width = 9, height = 4, units = "in",
       device = "pdf")

add_model_cluster_os <- fit_save_model(cluster_df %>% 
                                    dplyr::filter(!extent_of_tumor_resection %in% c("Not Reported", "Unavailable")),
                                    # %>%
                              #      dplyr::mutate(plot_group = fct_relevel(plot_group,
                               #                                             c("Other high-grade glioma", "Atypical Teratoid Rhabdoid Tumor",
                                #                                              "DIPG or DMG", "Ependymoma", "Mesenchymal tumor",
                                 #                                             "Other CNS embryonal tumor", "Low-grade glioma"))),
                              terms = "extent_of_tumor_resection+age_at_diagnosis_years+plot_group+CLK1_TPM_group",
                               file.path(results_dir, paste0( "cox_OS_additive_terms_subtype_", safe_each, "_clk1_tpm_group.RDS")),
                               "multivariate",
                               years_col = "OS_years",
                               status_col = "OS_status")

forest_cluster_clk1_os <- plotForest(readRDS(file.path(results_dir, paste0( "cox_OS_additive_terms_subtype_", safe_each, "_clk1_tpm_group.RDS"))))

ggsave(file.path(plot_dir, paste0( "forest_add_OS_", safe_each, "_histology_resection_clk1_tpm_group.pdf")),
       forest_cluster_clk1_os,
       width = 9, height = 4, units = "in",
       device = "pdf")

#Assess EFS and OS by CLK1 PSI group in multivariate models and generate forest plots

add_model_cluster_efs <- fit_save_model(cluster_df %>% 
                                      dplyr::filter(extent_of_tumor_resection != "Unavailable",
                                                    CLK1_PSI_group %in% c("High CLK1 PSI", "Low CLK1 PSI")),
                                      # %>%
                                     # dplyr::mutate(plot_group = fct_relevel(plot_group,
                                      #                                      c("Other high-grade glioma", "Atypical Teratoid Rhabdoid Tumor",
                                       #                                       "DIPG or DMG", "Ependymoma", "Mesenchymal tumor",
                                        #                                      "Other CNS embryonal tumor", "Low-grade glioma"))),
                              terms = "extent_of_tumor_resection+age_at_diagnosis_years+plot_group+CLK1_PSI_group",
                               file.path(results_dir, paste0( "cox_EFS_additive_terms_subtype_", safe_each, "_clk1_psi_group.RDS")),
                               "multivariate",
                               years_col = "EFS_years",
                               status_col = "EFS_status")

forest_cluster_clk1_efs <- plotForest(readRDS(file.path(results_dir, paste0( "cox_EFS_additive_terms_subtype_", safe_each, "_clk1_psi_group.RDS"))))

ggsave(file.path(plot_dir, paste0( "forest_add_EFS_", safe_each, "_histology_resection_clk1_psi_group.pdf")),
       forest_cluster_clk1_efs,
       width = 9, height = 4, units = "in",
       device = "pdf")

add_model_cluster_os <- fit_save_model(cluster_df %>% 
                                    dplyr::filter(!extent_of_tumor_resection %in% c("Not Reported", "Unavailable")), 
                                    # %>%
                                  #  dplyr::mutate(plot_group = fct_relevel(plot_group,
                                   #                                         c("Other high-grade glioma", "Atypical Teratoid Rhabdoid Tumor",
                                    #                                          "DIPG or DMG", "Ependymoma", "Mesenchymal tumor",
                                     #                                         "Other CNS embryonal tumor", "Low-grade glioma"))),
                              terms = "extent_of_tumor_resection+age_at_diagnosis_years+plot_group+CLK1_PSI_group",
                               file.path(results_dir, paste0( "cox_OS_additive_terms_subtype_", safe_each, "_clk1_psi_group.RDS")),
                               "multivariate",
                               years_col = "OS_years",
                               status_col = "OS_status")

forest_cluster_clk1_os <- plotForest(readRDS(file.path(results_dir, paste0( "cox_OS_additive_terms_subtype_", safe_each, "_clk1_psi_group.RDS"))))

ggsave(file.path(plot_dir, paste0( "forest_add_OS_", safe_each, "_histology_resection_clk1_psi_group.pdf")),
       forest_cluster_clk1_os,
       width = 9, height = 4, units = "in",
       device = "pdf")

# Assess EFS and OS by CLK1 ex 4 TPM in multivariate models and generate forest plots

add_model_cluster_efs <- fit_save_model(cluster_df %>% 
                                      dplyr::filter(extent_of_tumor_resection != "Unavailable"), 
                                   #   %>%
                                    #  dplyr::mutate(plot_group = fct_relevel(plot_group,
                                     #                                       c("Other high-grade glioma", "Atypical Teratoid Rhabdoid Tumor",
                                      #                                        "DIPG or DMG", "Ependymoma", "Mesenchymal tumor",
                                       #                                       "Other CNS embryonal tumor", "Low-grade glioma"))),
                              terms = "extent_of_tumor_resection+age_at_diagnosis_years+plot_group+CLK1_Ex4_TPM",
                               file.path(results_dir, paste0( "cox_EFS_additive_terms_subtype_", safe_each, "_clk1_tpm.RDS")),
                               "multivariate",
                               years_col = "EFS_years",
                               status_col = "EFS_status")

forest_cluster_clk1_efs <- plotForest(readRDS(file.path(results_dir, paste0( "cox_EFS_additive_terms_subtype_", safe_each, "_clk1_tpm.RDS"))))

ggsave(file.path(plot_dir, paste0( "forest_add_EFS_", safe_each, "_histology_resection_clk1_tpm.pdf")),
       forest_cluster_clk1_efs,
       width = 9, height = 4, units = "in",
       device = "pdf")

add_model_cluster_os <- fit_save_model(cluster_df %>% 
                                    dplyr::filter(!extent_of_tumor_resection %in% c("Not Reported", "Unavailable")), 
                                    #%>%
                                 #   dplyr::mutate(plot_group = fct_relevel(plot_group,
                                  #                                          c("Other high-grade glioma", "Atypical Teratoid Rhabdoid Tumor",
                                   #                                           "DIPG or DMG", "Ependymoma", "Mesenchymal tumor",
                                    #                                          "Other CNS embryonal tumor", "Low-grade glioma"))),
                              terms = "extent_of_tumor_resection+age_at_diagnosis_years+plot_group+CLK1_Ex4_TPM",
                               file.path(results_dir, paste0( "cox_OS_additive_terms_subtype_", safe_each, "_clk1_tpm.RDS")),
                               "multivariate",
                               years_col = "OS_years",
                               status_col = "OS_status")

forest_cluster_clk1_os <- plotForest(readRDS(file.path(results_dir, paste0( "cox_OS_additive_terms_subtype_", safe_each, "_clk1_tpm.RDS"))))

ggsave(file.path(plot_dir, paste0( "forest_add_OS_", safe_each, "_histology_resection_clk1_tpm.pdf")),
       forest_cluster_clk1_os,
       width = 9, height = 4, units = "in",
       device = "pdf")

# Assess EFS and OS by CLK1 ex 4 PSI in multivariate models and generate forest plots

add_model_cluster_efs <- fit_save_model(cluster_df %>% 
                                      dplyr::filter(extent_of_tumor_resection != "Unavailable"),
                                      # %>%
                                 #     dplyr::mutate(plot_group = fct_relevel(plot_group,
                                  #                                          c("Other high-grade glioma", "Atypical Teratoid Rhabdoid Tumor",
                                   #                                           "DIPG or DMG", "Ependymoma", "Mesenchymal tumor",
                                    #                                          "Other CNS embryonal tumor", "Low-grade glioma"))),
                              terms = "extent_of_tumor_resection+age_at_diagnosis_years+plot_group+CLK1_ex4_PSI",
                               file.path(results_dir, paste0( "cox_EFS_additive_terms_subtype_", safe_each, "_clk1_psi.RDS")),
                               "multivariate",
                               years_col = "EFS_years",
                               status_col = "EFS_status")

forest_cluster_clk1_efs <- plotForest(readRDS(file.path(results_dir, paste0( "cox_EFS_additive_terms_subtype_", safe_each, "_clk1_psi.RDS"))))

ggsave(file.path(plot_dir, paste0( "forest_add_EFS_", safe_each, "_histology_resection_clk1_psi.pdf")),
       forest_cluster_clk1_efs,
       width = 9, height = 4, units = "in",
       device = "pdf")

add_model_cluster_os <- fit_save_model(cluster_df %>% 
                                    dplyr::filter(!extent_of_tumor_resection %in% c("Not Reported", "Unavailable")),
                                    # %>%
                               #     dplyr::mutate(plot_group = fct_relevel(plot_group,
                                #                                            c("Other high-grade glioma", "Atypical Teratoid Rhabdoid Tumor",
                                 #                                             "DIPG or DMG", "Ependymoma", "Mesenchymal tumor",
                                  #                                            "Other CNS embryonal tumor", "Low-grade glioma")),                                 ),
                              terms = "extent_of_tumor_resection+age_at_diagnosis_years+plot_group+CLK1_ex4_PSI",
                               file.path(results_dir, paste0( "cox_OS_additive_terms_subtype_", safe_each, "_clk1_psi.RDS")),
                               "multivariate",
                               years_col = "OS_years",
                               status_col = "OS_status")

forest_cluster_clk1_os <- plotForest(readRDS(file.path(results_dir, paste0( "cox_OS_additive_terms_subtype_", safe_each, "_clk1_psi.RDS"))))

ggsave(file.path(plot_dir, paste0( "forest_add_OS_", each, "_histology_resection_clk1_psi.pdf")),
       forest_cluster_clk1_os,
       width = 9, height = 4, units = "in",
       device = "pdf")
}
```


Print session info

```{r}
sessionInfo()
```